The human mind is uncannily adept at identifying patterns and images. It can readily recognize most objects, regardless of how they are oriented. Thus, even those young of years or lacking in mental capacity can recognize a familiar piece of candy at any angle of presentation.
What comes to the mind so easily can be painstakingly difficult to teach a computer. Machine vision is one example. Software engineers have long labored to program these machines to identify objects in digital images. Though these efforts have paid off, the going has been slow. It is fair to estimate that billions of lines of programming instructions have been thrown away in the effort.
Discarded along the way were the early machine vision programs that were each written for a specific application. These programs were abandoned as modular programming came to the fore. Software engineering, in general, and machine vision, in particular, benefitted from the new thinking. Libraries were developed containing hundreds of small, reusable image analysis algorithms that could be invoked on a mix-and-match basis. Relying on these, software engineers began to construct shorter, more reliable and more easily debugged programs.
Common to these libraries is the so-called affine transformation tool, which resizes and rotates images. It is typically used in preprocessing, to prepare an image for automated analysis by other machine vision tools.
Affine transforms can be used, for example, to simplify the recognition of part numbers stamped on articles on a conveyor belt. Once the overall position and orientation of each part is determined, e.g., using coarse image processing techniques, an affine transform is applied to "artificially" rotate and size the region expected to contain the stamping. That portion of the image can then be interpreted through the use of character recognition software. The use of an affine transform in this manner is preferable, for example, to physically moving and reorienting the article, e.g., with a robotic arm.
Conventional prior art techniques suggest that affine transforms can be accomplished by mapping a source image into a destination image in a single pass. For every pixel location in the destination image, a corresponding location in the source image is identified. In a simplistic example, every pixel coordinate position in the destination image maps directly to an existing pixel in the source. Thus, for example, the pixel at coordinate (4,10) in the source maps to coordinate (2,5) in the destination; the pixel at (6,10) in the source, to (3,5) in the destination; and so on.
Reality is not so easy. Rarely do pixels in the source map directly to pixel positions in the destination. Thus, for example, a pixel at coordinate (4,10) in the source may map to a location (2.5, 5.33) in the destination. This can be problematic insofar as it requires interpolation to determine appropriate pixel intensities for the mapped coordinates. In the example, an appropriate intensity might be determined as a weighted average of the intensities for the source pixel locations (2,5), (3,5), (2,6), and (3,6).
The interpolation of thousands of such points is time consuming, even for a computer. Conventional affine transform tools must typically examine at least four points in the source image to generate each point in the destination image. This is compounded for higher-order transformations, which can require examination of many more points for each interpolation.
Although prior art has suggested the use of multiple passes (i.e., so-called separable techniques) in performing specific transformations, such as rotation, no suggestion is made as to how this might be applied to general affine transforms, e.g., involving simultaneous rotation, scaling, and skew.
Accordingly, an object of this invention is to provide improved systems for image processing and, more particularly, improved methods and apparatus for general affine transformation, e.g., for the simultaneous rotation, translation and scaling of images.
Another object of the invention is to provide such methods and apparatus as permit rapid analysis of images, without undue consumption of resources.
Still another object of the invention is to provide such methods and apparatus as are readily adapted to implementation conventional digital data processing apparatus, e.g., such as those equipped with commercially available superscalar processors--such as the Intel Pentium MMX or Texas Instruments C80 microprocessors.